Linear Regression (Service Level) 2

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as seabornInstance

from sklearn.linear_model import LinearRegression

from sklearn.model_selection import train_test_split

#from sklearn.feature_selection import SelectFromModel

from sklearn import metrics

from sklearn.metrics import classification_report

from sklearn.metrics import confusion_matrix

ServiceLevel = pd.read_csv("Service Level.csv")

#print(ServiceLevel.head())

#print(ServiceLevel.shape)

#print(ServiceLevel.describe())

#print(ServiceLevel.isnull().any())

y = ServiceLevel['SL'].values # Create arrays for the feature variable

X = ServiceLevel.drop(['SL','Date'], axis=1).values # Create arrays for the response variables

#X = ServiceLevel[['Volume Offered', 'Answered', 'Call Answered Within 20 Secs',

                 # 'Actual Staffing', 'AHT', 'MY AHT']].values # Create arrays for the response variables

# Check the average value of target (SL) column

plt.figure(figsize=(15,10))

plt.tight_layout()

seabornInstance.distplot(ServiceLevel['SL'])

#plt.show()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42) # Split into training and test set

regressor = LinearRegression()

regressor.fit(X_train, y_train) # training the algorithm

print(regressor.intercept_) # retrieve the intercept

print(regressor.coef_) # retrieve the slop

y_pred = regressor.predict(X_test)

df = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten()})

#print(df)

#print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))

#print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))

#print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))

# Mean Absolute Error: 0.0707227818477136

# Mean Squared Error: 0.009289545553397342

# Root Mean Squared Error: 0.09638228858767228

# Predicted SL

value = [((500*-1.62258670e-03) + (480*1.54044403e-03) + (150*2.09853820e-03) +

         (600*-8.34715959e-04) + (700*-9.69616262e-05)) + 1.2884114995794873]

print(value)